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Verified signature# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ha1dir/sentimen-indobert") # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ha1dir/sentimen-indobert") model = AutoModelForSequenceClassification.from_pretrained("Ha1dir/sentimen-indobert")040a0e2 -
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Verified signature# IndoBERT Sentiment Analysis Model Model ini adalah hasil fine-tuning model IndoBERT base untuk tugas klasifikasi sentimen bahasa Indonesia. ## Penggunaan from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "Ha1dir/sentimen-indobert" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) ## Label Kelas - Positive : 0 - Negative :1 - Neutral : 2 ## Tentang Model - Base Model: indobenchmark/indobert-base-p1 - Training Epochs: 5 - Optimizer: Adam, LR = 3e-6 ## Hasil Training (Epoch 1) TRAIN LOSS: 0.2962 Acc: 0.8896 Precision: 0.8893 Recall: 0.8896 F1: 0.8876 (Epoch 2) TRAIN LOSS: 0.1450 Acc: 0.9514 Precision: 0.9513 Recall: 0.9514 F1: 0.9513 (Epoch 3) TRAIN LOSS: 0.1053 Acc: 0.9646 Precision: 0.9646 Recall: 0.9646 F1: 0.9646 (Epoch 4) TRAIN LOSS: 0.0722 Acc: 0.9781 Precision: 0.9781 Recall: 0.9781 F1: 0.9781 (Epoch 5) TRAIN LOSS: 0.0468 Acc: 0.9874 Precision: 0.9874 Reca…6841b57 -
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